Swelling behavior and release features were also studied at different pH values. The swelling of the beads and their SR-oil release were negligible for the first 2 h in simulated gastric fluid (pH 1.2), and appreciable in simulated intestinal fluid (pH 6.8). The release data were fitted by various equations to define the release kinetic mechanism. In addition, the selected formulation (F16) was stable to the oxidation not only during the formulation process, but also after 3 months of storage at room temperature. In summary, these polynucleate alginate beads, produced by prilling technique, are promising systems for improving the intestinal specific delivery and bioavailability of health-promoting bioactive SR-oil.Customization of pharmaceutical products is a central requirement for personalized medicines. However, the existing processing and supply chain solutions do not support such manufacturing-on-demand approaches. In order to solve this challenge, three-dimensional (3D) printing has been applied for customization of not only the dose and release characteristics, but also appearance of the product (e.g., size and shape). A solution for customization can be realized via non-expert-guided processing of digital designs and drug dose. This study presents a proof-of-concept computational algorithm which calculates the optimal dimensions of grid-like orodispersible films (ODFs), considering the recommended dose. Further, the algorithm exports a digital design file which contains the required ODF configuration. Cannabidiol (CBD) was incorporated in the ODFs, considering the simple correspondence between the recommended dose and the patient's weight. The ODFs were 3D-printed and characterized for their physicochemical, mechanical, disintegration and drug release properties. The algorithm was evaluated for its accuracy on dose estimation, highlighting the reproducibility of individualized ODFs. The in vitro performance was principally affected by the thickness and volume of the grid-like structures. The concept provides an alternative approach that promotes automation in the manufacturing of personalized medications in distributed points of care, such as hospitals and local pharmacies.Naloxone is an opioid receptor antagonist that can eradicate all pre-indications of the toxicity and inverse the opioid overdose. However, oral administration of naloxone offers limitations such as its extensive first-pass metabolism that results in poor therapeutic effects. In order to resolve this issue, we developed intranasal solid-lipid nanoparticles in which naloxone was incorporated for the higher brain disposition of naloxone with superior therapeutic effects for the reversal of toxicity of opioid overdose. The preparation of naloxone loaded solid-lipid nanoparticles was done by employing the solvent evaporation method. Later, the designed formulation was optimized by Quality by Design approach, specifically, Box-Behnken method. https://www.selleckchem.com/products/PHA-665752.html The composition of optimized formulation was Glyceryl monostearate as a solid lipid (40 mg), Pluronic127 (0.5%) and Tween 80 (0.1%) as a surfactant and co-surfactant, respectively. Furthermore, the characterization of optimized formulation was achieved in terms of particle siztion and histopathological proved the plausible potential of naloxone-loaded solid-lipid nanoparticles in terms of safety as no toxicity was noticed even after the administration of the three-folds dose of the normal dose. Therefore, considering all these findings, it could be easy to say that these developed naloxone-loaded solid-lipid nanoparticles could be administrated via intranasal route and can act as successful novel nanoformulation for the effective treatment of opioid overdose.Radiation therapy (RT) is used for pediatric craniopharyngioma in the definitive, adjuvant, or salvage settings. Proton RT may be useful owing to tumor proximity to eloquent anatomy. We report clinical outcomes for a large cohort treated with proton therapy.
We conducted a retrospective review of pediatric patients (?21 years) treated with surgery and proton therapy for craniopharyngioma between August 2002 and October 2018. Clinical characteristics, treatment course, and outcomes were recorded. Acute toxicity was graded using Common Terminology Criteria for Adverse Events, version 5.0. Late toxicity was assessed using neuroendocrine, neuro-ophthalmologic, and neuropsychological testing.
Among 77 patients, median age at diagnosis was 8.6 years (range, 1.3-20); median age at radiation was 9.6 years (range, 2.3-20.5). Most common presenting symptoms were headache (58%), visual impairment (55%), and endocrinopathy (40%). Patients underwent a median of 2 surgical interventions (range, 1-7) before protons. Ane and ophthalmology follow-up is necessary, and neuropsychological testing may identify patients at risk for treatment-related cognitive and adaptive functioning changes.
Surgery and proton therapy results in excellent disease control for pediatric craniopharyngioma. Severe acute toxicity is rare. Late toxicities from tumor, surgery, and radiation remain prevalent. Endocrine and ophthalmology follow-up is necessary, and neuropsychological testing may identify patients at risk for treatment-related cognitive and adaptive functioning changes.The main objective of the present study was to integrate F-FDG-PET/CT radiomics with multiblock discriminant analysis for predicting circulating tumor cells (CTCs) in early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic body radiation therapy (SBRT).
Fifty-six patients with stage I NSCLC treated with SBRT underwent F-FDG-PET/CT imaging pre-SBRT and post-SBRT (median, 5 months; range, 3-10 months). CTCs were assessed via a telomerase-based assay before and within 3 months after SBRT and dichotomized at 5 and 1.3 CTCs/mL. Pre-SBRT, post-SBRT, and delta PET/CT radiomics features (n = 1548 × 3/1562 × 3) were extracted from gross tumor volume. Seven feature blocks were constructed including clinical parameters (n = 12). Multiblock data integration was performed using block sparse partial least squares-discriminant analysis (sPLS-DA) referred to as Data Integration Analysis for Biomarker Discovery Using Latent Components (DIABLO) for identifying key signatures by maximizing common information between different feature blocks while discriminating CTC levels.